{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solutions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Activation (sigmoid) function\n",
    "def sigmoid(x):\n",
    "    return 1 / (1 + np.exp(-x))\n",
    "\n",
    "def output_formula(features, weights, bias):\n",
    "    return sigmoid(np.dot(features, weights) + bias)\n",
    "\n",
    "def error_formula(y, output):\n",
    "    return - y*np.log(output) - (1 - y) * np.log(1-output)\n",
    "\n",
    "def update_weights(x, y, weights, bias, learnrate):\n",
    "    output = output_formula(x, weights, bias)\n",
    "    d_error = y - output\n",
    "    weights += learnrate * d_error * x\n",
    "    bias += learnrate * d_error\n",
    "    return weights, bias"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
